Direct Discrimination and Indirect Discrimination: A Distinction with a Difference
Bibliographic record
Abstract
Since Meiorin, it can be tempting to think that in Canadian law, the distinction between direct discrimination and indirect discrimination is now a distinction without a difference. The same analytical framework applies to both kinds of discrimination, and both can yield liability, so one might think that focusing on the distinction pointlessly distracts from the substantive concerns of discrimination law. However, I take a different view. In the context of Canadian human rights codes, the distinction remains significant. Despite attempts to abandon the distinction, the distinction seems to hold intuitive appeal and carries practical benefits. I submit that it is a distinction with a difference: it encourages adjudicators to consider more carefully discrimination without discriminatory intent, thus identifying cases of genuine discrimination they might otherwise miss.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.055 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".